1. Field of the Invention
The present invention relates to user profiling and more particularly to generating a user interests profile based upon the computing behavior of a corresponding user.
2. Description of the Related Art
Content browsing refers to the retrieval of content from a content source through a content browser from over a computer communications network. The most common form of content browsing pertains to Web page retrieval from the World Wide Web of documents, known by most as simply the “Web”. A large portion of the Web incorporates personalized delivery of content. Personalization ranges from recalling basic personal information such as name and address to consummate an e-commerce transaction, to complex and detailed demographic data and psychographic data such as attributes relating to personality, values, attitudes, interests, musical tastes, travel preferences, or lifestyles. Content personalization more recently has acted as a filter to the vast amount of information accessible over the global Internet. Specifically, the vast amount of information can result in “information overload” for the end user.
To facilitate the personalization of content delivery to different individuals, for more than a decade content providers have utilized the venerable “cookie”. A “cookie” as it is well known in the art is a text string stored by a Web browser. A cookie consists of one or more name-value pairs containing bits of information, which may be encrypted for information privacy and data security purposes. In operation, the cookie is sent as a hypertext transfer protocol (HTTP) header by a web server to a web browser and then sent back unchanged by the Web browser each time the Web browser accesses the Web server.
Despite the desirability of content personalization, many end users of the Web have grown to mistrust the use of the cookie. This mistrust will be apparent in the embedded “disable cookie” feature of most commercially available content browsers. Yet, much of this mistrust is misguided because as text, cookies are not executable and since cookies are not executed, cookies cannot replicate themselves and are not viruses. However, due to the content browser mechanism to set and read cookies, cookies can be used as spyware. In fact, modern anti-spyware applications warn end users about some cookies because cookies can be used to track end users—a privacy concern. Thus, while most modern content browsers permit the end user to opt to reject the use of cookies, or more reasonably, a time period during which a cookie is to be maintained, rejecting cookies renders some Web sites unusable.
User profiling provides a way for end users to experience personalized content with respect to content consumption over the computer communications network without relying upon the use of the venerable cookie. A user interests profile also referred to as an interest contour is a collection of personal data associated with a specific user. User profiling refers to the process of constructing a profile based upon a set of data associated with a target user. The user interests profile when accessed by a content provider, in turn, can provide personalized content to the target user. Web personalization models include rules-based filtering, based on “if this, then that” rules processing, and collaborative filtering, which serves relevant material to customers by combining their own personal preferences with the preferences of like-minded others.
Modern user profiling systems, implemented in an attempt to address information overload, are not adequate for delivering content which is relevant to the user, the interests of the user and the assignments or roles of the user. In this regard, user profiling systems generally rely upon the selection of interests by the user from a list of various interests. The user profiling systems then rely upon the user to maintain the accuracy and relevancy of the profile interests or career roles of the user change. Further, modern user profiling systems often require the user to complete multiple profile/interest lists. This leaves the user dissatisfied as in an enterprise environment the user expects that the enterprise applications will be interconnected and should be able to “net out” the user's interests based on the history of the activities, assignments, usage patterns and social networks of the end user. In addition, the user may not fully understand the taxonomy that underpins the selection of interests in various enterprise applications that can use different terminology to describe similar areas of interest. Historically, this approach has proven to be ineffective with profile/interest lists quickly becoming outdated.